knitr::opts_chunk$set(
    echo = TRUE,
    message = FALSE,
    warning = FALSE,
    dev = c("png")
)

Introduction

Load packages

library(Seurat)
library(dplyr)
library(patchwork)
library(DT)
library(SCpubr)
library(tibble)
library(reshape2)
library(viridis)
library(ggpubr)
library(ggplot2)

Open corrected counts and annotated Seurat object

Load the corrected (SoupX), normalized (SCTransformed) and annotated (Twice mapped - Tabula Sapiens Skin reference and PBMC azimuth reference) data.

srat <- readRDS(params$path_to_data)
meta <- srat@meta.data
meta$WHO <- "SD"
meta$WHO[meta$patient %in% c("NeoBCC007_post", "NeoBCC008_post", "NeoBCC012_post", "NeoBCC017_post")] <- "CR"
meta$WHO[meta$patient %in% c("NeoBCC004_post", "NeoBCC006_post", "NeoBCC010_post", "NeoBCC011_post")] <- "PR"
srat <- AddMetaData(srat, meta$WHO, col.name = "WHO")
srat$WHO <- factor(srat$WHO, levels = c("CR", "PR", "SD"))

Define levels and colors

srat@meta.data$anno_l1 <- factor(srat@meta.data$anno_l1, levels=c("other",
                                                                 "Mast cells",
                                                                 "Mono-Mac",
                                                                 "LC",
                                                                 "DC",
                                                                 "pDC",
                                                                 "Plasma cells",
                                                                 "B cells" ,
                                                                 "Proliferating cells",
                                                                 "Natural killer cells",
                                                                 "CD8+ T cells",
                                                                 "Tregs",
                                                                  "CD4+ T cells" ,
                                                                 "Melanocytes",
                                                                 "Endothelial cells",
                                                                 "Fibroblasts",
                                                                 "Keratinocytes",
                                                                 "Malignant cells"))
colors <- c("Malignant cells" = "#bd0026",
           "Keratinocytes" = "#dfc27d",
           "Fibroblasts" = "#f6e8c3",
           "Endothelial cells" = "#54278f",
           "Melanocytes" = "#a65628",
           "CD4+ T cells" = "#b8e186",
           "Tregs" = "#ae017e",
           "CD8+ T cells" = "#fbb4ae",
           "Proliferating cells" = "#b3cde3",
           "Natural killer cells" = "#9e9ac8",
           "B cells" = "#7bccc4",
           "Plasma cells" = "#35978f",
           "pDC" = "#fe9929",
           "DC" = "#e7298a",
           "LC" = "yellow" ,
           "Mono-Mac" = "#fec44f",
           "Mast cells" = "#bf812d",
           "other" = "#bdbdbd")

Figure 3a

p <- SCpubr::do_DimPlot(sample = srat, 
                  colors.use = colors, 
                  group.by = "anno_l1", 
                  pt.size=0.5, label = TRUE, 
                  repel = TRUE, 
                  legend.position = "none",  
                  label.color = "black") + 
     theme_minimal() + 
     NoLegend() + 
     theme(text = element_text(size=20))

p

DT::datatable(p$data, 
              caption = ("Figure 3a"),
              extensions = 'Buttons', 
              options = list( dom = 'Bfrtip',
              buttons = c( 'csv', 'excel')))

Figure 3b

genes <- list( 
               "Mal." = c("KRT17", "EPCAM", "BCAM"),
               "Kerati." = c("FGFBP1", "KRT1", "KRT6A"),
               "Fibro." = c("COL1A1", "COL1A2", "COL6A2"),
               "E" = c("VWF"),
               "Mel" = c("MLANA", "PMEL"),
               "CD4+T" = c("CD3E","CD2", "CD4"  ),
               "Tregs" = c("IL2RA", "CD25", "FOXP3", "TNFRSF4"),
               "CD8+T" = c("CD8A", "GZMA"),
               "NK" = c( "KLRC1", "PRF1", "GNLY"),
               "P" = c("MKI67"),
               "B" = c("MS4A1", "CD19"),
               "Plasma" = c("IGKC", "CD38", "SDC1"),
               "pDC" = c( "IRF8", "CLEC4C"),
               "DC" = c("LAMP3", "CCR7"),
               "LC" = c("CD1A", "CD207"),
               "Mono-Mac" = c("CD68",  "CD14" ),
               "Mast" = c("KIT", "SOCS1"))


p <- SCpubr::do_DotPlot(sample = srat,  
                        features = genes, 
                        group.by = "anno_l1",
                        font.size = 25, 
                        legend.length = 4,  
                        legend.type = "colorbar", 
                        dot.scale = 8,
                        sequential.palette ="PiYG",
                        scale = TRUE,
                        sequential.direction = -1)
p

DT::datatable(p$data, 
              caption = ("Figure 3b"),
              extensions = 'Buttons', 
              options = list( dom = 'Bfrtip',
              buttons = c( 'csv', 'excel')))

Session Info

sessionInfo()
## R version 4.3.0 (2023-04-21)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Europe/Vienna
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] stringr_1.5.1         msigdbr_7.5.1         DOSE_3.26.2           org.Hs.eg.db_3.17.0  
##  [5] AnnotationDbi_1.62.2  IRanges_2.34.1        S4Vectors_0.38.2      Biobase_2.60.0       
##  [9] BiocGenerics_0.46.0   clusterProfiler_4.8.3 enrichplot_1.20.3     scales_1.3.0         
## [13] RColorBrewer_1.1-3    ggnewscale_0.4.10     tidyr_1.3.1           scRepertoire_1.10.1  
## [17] dittoSeq_1.12.2       canceRbits_0.1.6      ggpubr_0.6.0.999      ggplot2_3.5.1        
## [21] viridis_0.6.5         viridisLite_0.4.2     reshape2_1.4.4        tibble_3.2.1         
## [25] SCpubr_2.0.2          DT_0.32               patchwork_1.2.0       dplyr_1.1.4          
## [29] Seurat_5.0.3          SeuratObject_5.0.1    sp_2.1-3             
## 
## loaded via a namespace (and not attached):
##   [1] fs_1.6.4                    matrixStats_1.2.0           spatstat.sparse_3.0-3      
##   [4] bitops_1.0-7                HDO.db_0.99.1               httr_1.4.7                 
##   [7] doParallel_1.0.17           tools_4.3.0                 sctransform_0.4.1          
##  [10] backports_1.4.1             utf8_1.2.4                  R6_2.5.1                   
##  [13] vegan_2.6-4                 lazyeval_0.2.2              uwot_0.1.16                
##  [16] mgcv_1.9-1                  permute_0.9-7               withr_3.0.0                
##  [19] gridExtra_2.3               progressr_0.14.0            cli_3.6.2                  
##  [22] spatstat.explore_3.2-7      fastDummies_1.7.3           scatterpie_0.2.1           
##  [25] isoband_0.2.7               labeling_0.4.3              sass_0.4.9                 
##  [28] spatstat.data_3.0-4         ggridges_0.5.6              pbapply_1.7-2              
##  [31] yulab.utils_0.1.4           gson_0.1.0                  stringdist_0.9.12          
##  [34] parallelly_1.37.1           limma_3.56.2                RSQLite_2.3.5              
##  [37] VGAM_1.1-10                 rstudioapi_0.16.0           generics_0.1.3             
##  [40] gridGraphics_0.5-1          ica_1.0-3                   spatstat.random_3.2-3      
##  [43] crosstalk_1.2.1             car_3.1-2                   GO.db_3.17.0               
##  [46] Matrix_1.6-5                ggbeeswarm_0.7.2            fansi_1.0.6                
##  [49] abind_1.4-5                 lifecycle_1.0.4             edgeR_3.42.4               
##  [52] yaml_2.3.8                  carData_3.0-5               SummarizedExperiment_1.30.2
##  [55] qvalue_2.32.0               Rtsne_0.17                  blob_1.2.4                 
##  [58] grid_4.3.0                  promises_1.2.1              crayon_1.5.2               
##  [61] miniUI_0.1.1.1              lattice_0.22-6              cowplot_1.1.3              
##  [64] KEGGREST_1.40.1             pillar_1.9.0                knitr_1.45                 
##  [67] fgsea_1.26.0                GenomicRanges_1.52.1        future.apply_1.11.1        
##  [70] codetools_0.2-19            fastmatch_1.1-4             leiden_0.4.3.1             
##  [73] glue_1.7.0                  downloader_0.4              ggfun_0.1.5                
##  [76] data.table_1.15.2           treeio_1.24.3               vctrs_0.6.5                
##  [79] png_0.1-8                   spam_2.10-0                 gtable_0.3.5               
##  [82] assertthat_0.2.1            cachem_1.1.0                xfun_0.43                  
##  [85] S4Arrays_1.0.6              mime_0.12                   tidygraph_1.3.1            
##  [88] survival_3.5-8              DElegate_1.2.1              SingleCellExperiment_1.22.0
##  [91] pheatmap_1.0.12             iterators_1.0.14            fitdistrplus_1.1-11        
##  [94] ROCR_1.0-11                 nlme_3.1-164                ggtree_3.13.0.001          
##  [97] bit64_4.0.5                 RcppAnnoy_0.0.22            evd_2.3-6.1                
## [100] GenomeInfoDb_1.36.4         bslib_0.6.2                 irlba_2.3.5.1              
## [103] vipor_0.4.7                 KernSmooth_2.23-22          DBI_1.2.2                  
## [106] colorspace_2.1-0            ggrastr_1.0.2               tidyselect_1.2.1           
## [109] bit_4.0.5                   compiler_4.3.0              SparseM_1.81               
## [112] DelayedArray_0.26.7         plotly_4.10.4               shadowtext_0.1.3           
## [115] lmtest_0.9-40               digest_0.6.35               goftest_1.2-3              
## [118] spatstat.utils_3.0-4        rmarkdown_2.26              XVector_0.40.0             
## [121] htmltools_0.5.8             pkgconfig_2.0.3             sparseMatrixStats_1.12.2   
## [124] MatrixGenerics_1.12.3       highr_0.10                  fastmap_1.2.0              
## [127] rlang_1.1.4                 htmlwidgets_1.6.4           shiny_1.8.1                
## [130] farver_2.1.2                jquerylib_0.1.4             zoo_1.8-12                 
## [133] jsonlite_1.8.8              BiocParallel_1.34.2         GOSemSim_2.26.1            
## [136] RCurl_1.98-1.14             magrittr_2.0.3              GenomeInfoDbData_1.2.10    
## [139] ggplotify_0.1.2             dotCall64_1.1-1             munsell_0.5.1              
## [142] Rcpp_1.0.12                 evmix_2.12                  babelgene_22.9             
## [145] ape_5.8                     reticulate_1.35.0           truncdist_1.0-2            
## [148] stringi_1.8.4               ggalluvial_0.12.5           ggraph_2.2.1               
## [151] zlibbioc_1.46.0             MASS_7.3-60.0.1             plyr_1.8.9                 
## [154] parallel_4.3.0              listenv_0.9.1               ggrepel_0.9.5              
## [157] forcats_1.0.0               deldir_2.0-4                Biostrings_2.68.1          
## [160] graphlayouts_1.1.1          splines_4.3.0               tensor_1.5                 
## [163] locfit_1.5-9.9              igraph_2.0.3                spatstat.geom_3.2-9        
## [166] cubature_2.1.0              ggsignif_0.6.4              RcppHNSW_0.6.0             
## [169] evaluate_0.23               foreach_1.5.2               tweenr_2.0.3               
## [172] httpuv_1.6.15               RANN_2.6.1                  purrr_1.0.2                
## [175] polyclip_1.10-6             future_1.33.2               scattermore_1.2            
## [178] ggforce_0.4.2               broom_1.0.5                 xtable_1.8-4               
## [181] tidytree_0.4.6              RSpectra_0.16-1             rstatix_0.7.2              
## [184] later_1.3.2                 gsl_2.1-8                   aplot_0.2.3                
## [187] beeswarm_0.4.0              memoise_2.0.1               cluster_2.1.6              
## [190] powerTCR_1.20.0             globals_0.16.3